R: Various (Regularized) t Statistics

regularizedt {st} | R Documentation

## Various (Regularized) t Statistics

### Description

These functions provide a simple interface to a variety of (regularized) t statistics that are commonly used in the analysis of high-dimensional case-control studies.

### Usage

    efront.stat(X, L, verbose=TRUE)
    efront.fun(L, verbose=TRUE)
    sam.stat(X, L)
    sam.fun(L)
    samL1.stat(X, L, method=c("lowess", "cor"), plot=FALSE, verbose=TRUE)
    samL1.fun(L, method=c("lowess", "cor"), plot=FALSE, verbose=TRUE)
    modt.stat(X, L)
    modt.fun(L)

### Arguments

`X` | data matrix. Note that the columns correspond to variables (“genes”) and the rows to samples.
`L` | factor containing class labels for the two groups.
`method` | determines how the smoothing parameter is estimated (applies only to improved SAM statistic `samL1`).
`plot` | output diagnostic plot (applies only to improved SAM statistic `samL1`).
`verbose` | print out some (more or less useful) information during computation.

### Details

`efront.*` computes the t statistic using the 90 % rule of Efron et al. (2001).

`sam.*` computes the SAM t statistic of Tusher et al. (2001). Note that this requires the additional installation of the “samr” package.

`samL1.*` computes the improved SAM t statistic of Wu (2005). Note that part of the code in this function is based on the R code providec by B. Wu.

`modt.*` computes the moderated t statistic of Smyth (2004). Note that this requires the additional installation of the “limma” package.

All the above statistics are compared relative to each other and relative to the shrinkage t statistic in Opgen-Rhein and Strimmer (2007).

### Value

The *.stat functions directly return the respective statistic for each variable.

The corresponding *.fun functions return a function that produces the respective statistics when applied to a data matrix (this is very useful for simulations).

### Author(s)

Rainer Opgen-Rhein and Korbinian Strimmer (http://strimmerlab.org).

### References

Opgen-Rhein, R., and K. Strimmer. 2007. Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Statist. Appl. Genet. Mol. Biol. 6:9. (http://dx.doi.org/10.2202/1544-6115.1252)

### See Also

`diffmean.stat`, `studentt.stat`, `shrinkt.stat`, `shrinkcat.stat`.

### Examples

    # load st library
    library("st")

    # load Choe et al. (2005) data
    data(choedata)
    X <- choe2.mat
    dim(X) # 6 11475
    L <- choe2.L
    L

    # L may also contain some real labels
    L = c("group 1", "group 1", "group 1", "group 2", "group 2", "group 2")

    # Efron t statistic (90 % rule)
    score = efront.stat(X, L)
    order(score^2, decreasing=TRUE)[1:10]
    # [1]  4790 10979 11068  1022    50   724  5762    43 10936  9939

    # sam statistic
    # (requires "samr" package)
    #score = sam.stat(X, L)
    #order(score^2, decreasing=TRUE)[1:10]
    #[1]  4790 10979  1022  5762    35   970    50 11068 10905  2693

    # improved sam statistic
    #score = samL1.stat(X, L)
    #order(score^2, decreasing=TRUE)[1:10]
    #[1]  1  2  3  4  5  6  7  8  9 10
    # here all scores are zero!

    # moderated t statistic
    # (requires "limma" package)
    #score = modt.stat(X, L)
    #order(score^2, decreasing=TRUE)[1:10]
    # [1]  4790 10979  1022  5762    35    50 11068   970 10905    43

    # shrinkage t statistic
    score = shrinkt.stat(X, L)
    order(score^2, decreasing=TRUE)[1:10]
    #[1] 10979 11068    50  1022   724  5762    43  4790 10936  9939

[Package st version 1.2.5 Index]